Python Tutorial : How to build a GLM?

Want to learn more? Take the full course at https://learn.datacamp.com/courses/generalized-linear-models-in-python at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Now you know that linear models are not suitable to accommodate different than continuous response data and provide rather strange results. In this lesson, you will learn how to overcome such problems in a quite elegant way with GLMs, which provide a unified framework for modeling data originating from the exponential family of densities which include Gaussian, Binomial, and Poisson, among others. There are three components of GLM, the random component which defines the response variable y and its probability distribution. As we saw previously there are different response data types to consider depending on your data problem. One important assumption here is that the observations y_1 through y_n are independent. The second component is the systematic component which defines which explanatory variables to include in the model. We can include p different explanatory variables. Note that it allows for interaction effects, where one variable depends on another and vice versa, or curvilinear effects, etc. Note that the RHS represents a linear combination of the explanatory variables. The third and final component is the link function, which connects the random and systematic components. It is the function of the expected value of the response variable which enables linearity in the parameters. By its construction it allows the mean of the response variable to be nonlinearly related to the explanatory variables. It is the link function that generalizes the linear model. Note that the choice of the link function is separate from the choice of random component. Let's review the most common data types and how they are represented in the GLM framework. One data type we are all very familiar with is continuous and approximately normally distribut